L
Lin Gu
Researcher at Huazhong University of Science and Technology
Publications - 85
Citations - 2076
Lin Gu is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Cloud computing & Scheduling (computing). The author has an hindex of 17, co-authored 74 publications receiving 1493 citations. Previous affiliations of Lin Gu include University of Aizu.
Papers
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Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System
TL;DR: A computation-efficient solution is proposed based on the formulation and validated by extensive simulation based studies to deal with the high computation complexity of fog computing supported software-defined embedded system.
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Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System
TL;DR: Fog computation and MCPS are integrated to build fog computing supported MCPS (FC-MCPS), and an LP-based two-phase heuristic algorithm is proposed that produces near optimal solution and significantly outperforms a greedy algorithm.
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Cost Minimization for Big Data Processing in Geo-Distributed Data Centers
TL;DR: This paper proposes a 2-D Markov chain and derives the average task completion time in closed-form and proposes an efficient solution to linearize it, which is validated by extensive simulation-based studies.
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Resource Management at the Network Edge: A Deep Reinforcement Learning Approach
TL;DR: This work designs and implements a mobility- aware data processing service migration management agent that can automatically learn the user mobility pattern and accordingly control the service migration among the edge servers to minimize the operational cost at runtime.
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A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers
TL;DR: The communication cost minimization problem for BDSP is formulated into a mixed-integer linear programming (MILP) problem and proved to be NP-hard, and a computation-efficient solution based on MILP is proposed.